# Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

arXiv: https://arxiv.org/abs/2101.06905 · HTML: https://mlsanity.com/paper/2101.06905/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/1608.04636/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [Local Differential Privacy for Deep Learning](https://mlsanity.com/paper/1908.02997/) (2019)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [When Mobile Blockchain Meets Edge Computing](https://mlsanity.com/paper/1711.05938/) (2017)
- [Writing Across the World's Languages: Deep Internationalization for Gboard, the Google Keyboard](https://mlsanity.com/paper/1912.01218/) (2019)
- [A Distributed Trust Framework for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2006.02456/) (2020)
- [Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model](https://mlsanity.com/paper/1805.06104/) (2018)
- [Privacy Preserving Location Data Publishing: A Machine Learning Approach](https://mlsanity.com/paper/1902.08934/) (2019)
- [RDP-GAN: A R'enyi-Differential Privacy based Generative Adversarial Network](https://mlsanity.com/paper/2007.02056/) (2020)

## Built on by (most cited first, 30 of 56)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Improving the Model Consistency of Decentralized Federated Learning](https://mlsanity.com/paper/2302.04083/) (2023)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [A Systematic Survey of Blockchained Federated Learning](https://mlsanity.com/paper/2110.02182/) (2021)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning](https://mlsanity.com/paper/2202.10938/) (2022)
- [Federated Learning Attacks and Defenses: A Survey](https://mlsanity.com/paper/2211.14952/) (2022)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Decentralized Directed Collaboration for Personalized Federated Learning](https://mlsanity.com/paper/2405.17876/) (2024)
- [Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training](https://mlsanity.com/paper/2305.15157/) (2023)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [A Fast Blockchain-based Federated Learning Framework with Compressed Communications](https://mlsanity.com/paper/2208.06095/) (2022)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality](https://mlsanity.com/paper/2401.07558/) (2024)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology](https://mlsanity.com/paper/2111.05505/) (2021)
- [Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control](https://mlsanity.com/paper/2211.02296/) (2022)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning](https://mlsanity.com/paper/2307.09263/) (2023)
- [Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity](https://mlsanity.com/paper/2308.03521/) (2023)
- [Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition](https://mlsanity.com/paper/2501.08007/) (2025)
- [DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning](https://mlsanity.com/paper/2208.00848/) (2022)

## Abstract (arXiv)

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy server, and external attacks. To address this issue, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts the trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training of the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds K, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients. Based on MNIST and Fashion-MNIST datasets, we show that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than 5%, and the optimized K based on the upper bound can effectively minimize the loss function.